400 citations · 533 across the 7 of their papers we have counts for
5 papers · 1 filter
Early Stopping is Nonparametric Variational Inference
Dougal Maclaurin, David Duvenaud, Ryan P. Adams
We show that unconverged stochastic gradient descent can be interpreted as a procedure that samples from a nonparametric variational approximate posterior distribution. This distri…
Gradient-based Hyperparameter Optimization through Reversible Learning
Dougal Maclaurin, David Duvenaud, Ryan P. Adams
Tuning hyperparameters of learning algorithms is hard because gradients are usually unavailable. We compute exact gradients of cross-validation performance with respect to all hype…
Raiders of the Lost Architecture: Kernels for Bayesian Optimization in Conditional Parameter Spaces
Kevin Swersky, David Duvenaud, Jasper Snoek +2
In practical Bayesian optimization, we must often search over structures with differing numbers of parameters. For instance, we may wish to search over neural network architectures…
Probabilistic ODE Solvers with Runge-Kutta Means
Michael Schober, David Duvenaud, Philipp Hennig
Runge-Kutta methods are the classic family of solvers for ordinary differential equations (ODEs), and the basis for the state of the art. Like most numerical methods, they return p…
Warped Mixtures for Nonparametric Cluster Shapes
Tomoharu Iwata, David Duvenaud, Zoubin Ghahramani
A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a mode…